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Accurate Identification of Unknown and Known Metabolic Mixture Components by Combining 3D NMR with Fourier Transform Ion Cyclotron Resonance Tandem Mass Spectrometry.

Title: Accurate Identification of Unknown and Known Metabolic Mixture Components by Combining 3D NMR with Fourier Transform Ion Cyclotron Resonance Tandem Mass Spectrometry.
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Name(s): Wang, Cheng, author
He, Lidong, author
Li, Da-Wei, author
Bruschweiler-Li, Lei, author
Marshall, Alan G, author
Brüschweiler, Rafael, author
Type of Resource: text
Genre: Journal Article
Text
Date Issued: 2017-10-06
Physical Form: computer
online resource
Extent: 1 online resource
Language(s): English
Abstract/Description: Metabolite identification in metabolomics samples is a key step that critically impacts downstream analysis. We recently introduced the SUMMIT NMR/mass spectrometry (MS) hybrid approach for the identification of the molecular structure of unknown metabolites based on the combination of NMR, MS, and combinatorial cheminformatics. Here, we demonstrate the feasibility of the approach for an untargeted analysis of both a model mixture and E. coli cell lysate based on 2D/3D NMR experiments in combination with Fourier transform ion cyclotron resonance MS and MS/MS data. For 19 of the 25 model metabolites, SUMMIT yielded complete structures that matched those in the mixture independent of database information. Of those, seven top-ranked structures matched those in the mixture, and four of those were further validated by positive ion MS/MS. For five metabolites, not part of the 19 metabolites, correct molecular structural motifs could be identified. For E. coli, SUMMIT MS/NMR identified 20 previously known metabolites with three or more H spins independent of database information. Moreover, for 15 unknown metabolites, molecular structural fragments were determined consistent with their spin systems and chemical shifts. By providing structural information for entire metabolites or molecular fragments, SUMMIT MS/NMR greatly assists the targeted or untargeted analysis of complex mixtures of unknown compounds.
Identifier: FSU_pmch_28795575 (IID), 10.1021/acs.jproteome.7b00457 (DOI), PMC5663437 (PMCID), 28795575 (RID), 28795575 (EID)
Keywords: 3D NMR HSQC-TOCSY, COLMAR database, NMR-MS hybrid approach, Metabolomics, Unknown metabolite identification
Grant Number: R01 GM066041, U24 DK097209
Publication Note: This NIH-funded author manuscript originally appeared in PubMed Central at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5663437.
Subject(s): Complex Mixtures/chemistry
Complex Mixtures/metabolism
Cyclotrons
Escherichia coli/chemistry
Escherichia coli/metabolism
Escherichia coli Proteins/chemistry
Escherichia coli Proteins/metabolism
Magnetic Resonance Spectroscopy
Metabolome/genetics
Metabolomics/methods
Molecular Structure
Tandem Mass Spectrometry
Persistent Link to This Record: http://purl.flvc.org/fsu/fd/FSU_pmch_28795575
Host Institution: FSU
Is Part Of: Journal of proteome research.
1535-3907
Issue: iss. 10, vol. 16

Choose the citation style.
Wang, C., He, L., Li, D. -W., Bruschweiler-Li, L., Marshall, A. G., & Brüschweiler, R. (2017). Accurate Identification of Unknown and Known Metabolic Mixture Components by Combining 3D NMR with Fourier Transform Ion Cyclotron Resonance Tandem Mass Spectrometry. Journal Of Proteome Research. Retrieved from http://purl.flvc.org/fsu/fd/FSU_pmch_28795575